Transforming maternal healthcare: Harnessing the power of artificial intelligence for improved outcomes and access

Artificial intelligence (AI) signifies advanced computer systems adept at tasks traditionally within the purview of human intelligence. This paper explores the transformative landscape of AI applications in healthcare, with a specific focus on risk assessment, predictive modeling, and remote monitoring to proactively address high-risk pregnancies. Aligned with Sustainable Development Goal (SDG) 3.1, our investigation underscores AI's pivotal role in advancing maternal outcomes, encapsulating recent research across domains such as complication prediction, healthcare access enhancement, clinical decision support systems, and fertility treatments. AI-driven models demonstrate efficacy in predicting preterm birth, gestational diabetes, preeclampsia, and other adverse outcomes through meticulous analysis of maternal health data, enabling timely interventions. In underserved regions, AI acts as a catalyst, enhancing accessibility to vital services like prenatal ultrasounds and health education through telemedicine platforms. The integration of AI decision support systems empowers healthcare providers with real-time, patient-specific assessments and recommendations derived from population data analysis. Within fertility medicine, AI proves instrumental in refining genetic screening, embryo viability selection, and optimizing in vitro fertilization success rates. Despite these advancements, challenges persist in regulatory policy, privacy safeguards, accuracy, and seamless integration into clinical workflows, necessitating prudent consideration before widespread implementation. So, ethically applied AI emerges as a transformative force, offering substantial opportunities to advance maternal healthcare significantly. By averting complications, broadening access, informing sound decisions, and optimizing fertility outcomes, AI stands as a promising ally. This comprehensive review encapsulates pivotal applications of this burgeoning technology, outlining potential directions for future research, thereby contributing to the realization of SDG 3.1.